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Canjun Zhao,1,* Jin Zhang,2,* Litian Ma,1,* Hao Wu,1 Hui Zhang,1 Jialin Su,1 Bizu Geng,1 Qinghua Yao,3 Jin Zheng1 1Department of Traditional Chinese Medicine, The Second Hospital Affiliated to Air Force Medical University, Xi’an, People’s Republic of China; 2First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China; 3Department of Integrated Traditional Chinese and Western Medicine, Zhejiang Cancer Hospital, Hangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jin ZhengDepartment of Traditional Chinese Medicine, The Second Hospital Affiliated to Air Force Medical University, Xi’an, Shaanxi Province 710038, People’s Republic of ChinaTel +86-2984777441Email zhengjin_jzhj@163.comQinghua YaoDepartment of Integrated Traditional Chinese and Western Medicine, Zhejiang Cancer Hospital, Gongshu District, Hangzhou, Zhejiang Province 310000, People’s Republic of ChinaTel +86-57188122012Email yaowhua1@163.comPurpose: We aim to investigate the role of Golgi phosphoprotein 3 (GOLPH3) and the possible regulation mechanism underlying lung adenocarcinoma (LADC).Methods: The level of GOLPH3 was performed by quantitative real time (qRT)-PCR, Western blot and immunohistochemistry. Patient survival rate was analyzed by Kaplan–Meier method. MTT was used to detect cell viability. The levels of p-serine/threonine-protein kinase (Akt), Akt, p-p65, p65 and β-catenin were determined by Western blot. Cell apoptosis was tested using flow cytometry. Angiogenesis was determined by in vitro angiogenesis assay. qPCR and Western blot were performed to identify apoptotic protein B-cell lymphoma 2 (Bcl-2)/Bcl-2-associated X protein (Bax) and vascular endothelial growth factor (VEGF).Results: GOLPH3 was highly expressed in LADC cell lines and tissues and was significantly correlated with poor overall survival among patients with LADC. Furthermore, GOLPH3 expression was reduced in A549 and H23 cells in a cisplatin-dependent manner. Silencing of GOLPH3 enhanced inhibition of A549 and H23 cells by cisplatin and suppressed the protein expression of p-Akt, while p-p65 expression remained stable. However, overexpression of GOLPH3 weakened the inhibition of A549 and H23 cells by cisplatin and improved the protein expression of p-Akt, while p-p65 expression remained stable. XAV939, an inhibitor of Wnt/β-catenin signaling pathway, decreased GOLPH3 overexpression-induced proliferation and enhanced cisplatin-induced angiogenesis inhibition and apoptosis, which was supported by the changes of VEGF, Bax and Bcl-2.Conclusion: GOLPH3 promotes proliferation capacity in LADC through activating the Wnt/β-catenin signaling pathway.Keywords: Golgi phosphoprotein 3, lung adenocarcinoma, Wnt/β-catenin, proliferation

Abstract Chest pain, a common initial symptom in hypertrophic cardiomyopathy (HCM) patients, is closely linked to myocardial ischemia, despite the absence of significant coronary artery stenosis. This study explored microvascular dysfunction in HCM patients by employing angiography-derived microcirculatory resistance (AMR) as a novel tool for comprehensive assessment. This retrospective analysis included HCM patients with chest pain as the primary symptom and control patients without cardiac hypertrophy during the same period. The AMR was computed through angiography, providing a wire-free and adenosine-free index for evaluating microcirculatory function. Propensity score matching ensured balanced demographics between groups. This study also investigated the correlation between the AMR and clinical outcomes by utilizing echocardiography and follow-up data. After matching, 76 HCM patients and 152 controls were analyzed. While there was no significant difference in the incidence of epicardial coronary stenosis, the AMR of three epicardial coronary arteries was markedly greater in HCM patients. The criterion of an AMR ≥ 250 mmHg*s/m was that 65.7% of HCM patients experienced coronary microvascular dysfunction (CMD). Independent risk factors for CMD included increased left ventricular (LV) wall thickness (OR = 1.209, 95% CI 1.013–1.443, p = 0.036). Furthermore, an AMR_LAD ≥ 250 mmHg*s/m had an increased cumulative risk of the endpoint (log-rank p = 0.023) and was an independent risk factor for the endpoint (HR = 11.64, 95% CI 1.13–120.03, p = 0.039), providing valuable prognostic insights.

To investigate the clinical value of echocardiographic detection in the prenatal early diagnosis of Scimitar syndrome (SS) in fetuses, and to develop better and more accurate management strategies for improved prognosis.

[Objective] Indium resources are an important supporting raw material for the development of China’s strategic emerging industries, and the study of the current status and dynamic evolution of the value flow of indium resources across the whole industry chain is of great strategic significance for the security of China’s indium resources and the sustainable development of its economy. [Methods] Based on the material flow framework of China’s indium resources in 2019, this paper utilizes the value chain analysis method and maps the value flow of China’s indium resources corresponding to the material flow process of indium resources according to the factor price theory, in order to evaluate and analyze the changes of value increase and decrease in the whole industry chain of China’s indium resources. [Results] (1) The total value of China’s indium resources industry chain shows an inverted U-shaped trend of increasing and then decreasing; (2) The stages with the highest value appreciation in the indium value chain are smelting and refining, with value increases of 922% and 1002% respectively. This is attributed to the substantial enhancement of indium resource grade due to the inputs of technology, labor, and energy, and that indium is a by-product metal with its associated zinc metal having relatively low value; (3) Although China is the world’s largest country in terms of indium resources, the indium resource industry chain still relies on imports to sustain its development; (4) Recycling of indium resources can have significant economic value. [Conclusion] Therefore, optimizing the indium resource industry chain, upgrading the production process and technology of high-end indium-containing products, and strengthening the recycling of indium-containing waste products are important ways to increase the value of indium resources in China.

The technology of power to hydrogen (P2H) and natural gas mixed hydrogen is highly regarded in theoretical research and engineering application in promoting the consumption of renewable energy and reducing carbon emissions. Targeting the park integrated energy system with high proportion renewable energy, an optimal configuration method of P2H considering natural gas mixed with hydrogen and cross seasonal storage is proposed. Firstly, the operational framework and energy flow relationship of the park integrated energy system are sorted out, and the mathematical models for the internal energy production, conversion, and storage equipment in the park are established. Secondly, a configuration model of P2H is established with the goal of optimizing the annual investment cost of equipment, the annual operating cost, and the carbon trading cost. Finally, the effectiveness of the proposed model is verified by an example analysis. The impact of changes in investment cost of electrolytic cells, the upper limit of mixed hydrogen volume fraction and cost weight coefficients of economy and low-carbon on the planned operation results are analyzed. The simulation results show that the proposed model can effectively improve the absorption capacity of renewable energy and reduce the overall economic cost and carbon emissions.

Photothermal therapy (PTT) of nanomaterials is an emerging novel therapeutic strategy for breast cancer. However, there exists an urgent need for appropriate strategies to enhance the antitumor efficacy of PTT and minimize damage to surrounding normal tissues. Piezo1 might be a promising novel photothermal therapeutic target for breast cancer. This study aims to explore the potential role of Piezo1 activation in the hyperthermia therapy of breast cancer cells and investigate the underlying mechanisms. Results showed that the specific agonist of Piezo1 ion channel (Yoda1) aggravated the cell death of breast cancer cells triggered by heat stress in vitro. Reactive oxygen species (ROS) production was significantly increased following heat stress, and Yoda1 exacerbated the rise in ROS release. GSK2795039, an inhibitor of NADPH oxidase 2 (NOX2), reversed the Yoda1-mediated aggravation of cellular injury and ROS generation after heat stress. The in vivo experiments demonstrate the well photothermal conversion efficiency of TiCN under the 1,064 nm laser irradiation, and Yoda1 increases the sensitivity of breast tumors to PTT in the presence of TiCN. Our study reveals that Piezo1 activation might serve as a photothermal sensitizer for PTT, which may develop as a promising therapeutic strategy for breast cancer.

Crop nitrogen (N) content reflects crop nutrient status and plays an important role in precision nutrient management. Accurate crop N content estimation from remote sensing has been well documented. However, the robustness (i.e., the ability of a model to perform consistently across various conditions) of these methods under varied soil conditions or different growth stages has rarely been considered. We proposed a hybrid method that integrates in-situ measurements and the data simulated by a mechanistic model to improve the estimation of maize N content. In-situ data included hyperspectral images collected by Unmanned Aerial Vehicle (UAV), and leaf and canopy N content (LNC and CNC). A mechanistic radiative transfer model (PROSAIL-PRO) was used to generate simulated data, i.e., canopy reflectance paired with target crop traits (i.e., LNC, CNC). We compared the performance from the hybrid method with a machine learning method (Gaussian Process Regression) and six different vegetation indices (VIs) on four in-situ datasets collected at three study sites from 2021 to 2022. Results show that the hybrid method consistently performed the best for LNC estimation across four testing datasets (RRMSE ranging from 10.08% to 10.84%). For CNC estimation, the hybrid method had the best estimation results on two out of the four testing datasets and performed comparably to the best method on the other two datasets (RRMSE ranging from 13.89% to 25.21%). Next, we assessed the estimation robustness of the hybrid method, the machine learning, and the best-VI by comparing the mean (µ) and standard deviation (σ) of RRMSE across diverse water and N treatments (condition #1) and different growth stages (condition #2). Among 16 total cases (two crop traits by four study sites by two conditions), the hybrid method had 11 cases of smallest µ and seven cases of smallest σ, outperforming the machine learning (0/16 for µ, 4/16 for σ) and the best-VI (5/16 for µ, 5/16 for σ). These results underscore the greater robustness of the hybrid method. This study highlights the potential of integrating in-situ measurements and simulated data to improve estimation accuracy and robustness for maize LNC and CNC. The promising performance of the hybrid method suggests its applicability to a broader range of crops and various crop traits.

Abstract Cancer-associated fibroblasts (CAFs) play a role in ovarian cancer (OV) evolution, immunosuppression and promotion of drug resistance. Exploring the value of CAFs-related biomarker in OV is of great importance. In the present work, we developed a CAFs-related index (CAFRI) based on an integrated analysis of single-cell and bulk RNA-sequencing and highlighted the value of CAFRI in predicting clinical outcomes in individuals with OV, tumour immune microenvironment (TIME) and response to immune checkpoint inhibitors (ICIs). The GSE151214 cohort was used for cell subpopulation localization and analysis, the TCGA-OV patients as a training set. Moreover, the ICGC-OV, GSE26193, GSE26712 and GSE19829 cohorts were used for the validation of CAFRI. The TIMER 2.0, CIBERSORT and ssGSEA algorithms were used for analysis of TIME characteristics based on the CAFRI. The GSVA, GSEA, GO, KEGG and tumour mutation burden (TMB) analyses were used for mechanistic exploration. Additionally, the IMvigor210 cohort was conducted to validate the predictive value of CAFRI on the efficacy of ICIs. Finally, CAFRI-based antitumour drug sensitivity was analysed. The findings demonstrate that the CAFRI can served as an excellent predictor of prognosis for individuals with OV, as well as identifying patients with different TIME characteristics, differentiating between immune ‘hot’ and ‘cold’ tumour populations, and providing new insights into the selection of ICIs and personalised treatment regimens. CAFRI provides new perspectives for the development of novel prognostic and immunotherapy efficacy predictive biomarkers for OV.

Abstract Purpose The purpose of this study was to provide a critical systematic review of the role of magnetic resonance imaging (MRI) as a noninvasive method to assess periprosthetic joint infections (PJIs). Methods The electronic databases PubMed and EMBASE were searched, since their inception up to March 27, 2022. The included studies evaluated the reproducibility and accuracy of MRI features to diagnose PJIs. The article quality assessment was conducted by the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Results Among 1909 studies identified in the initial search, 8 studies were eligible for final systematic review. The included studies evaluated the reproducibility and accuracy of MRI features to diagnose PJIs. Seven of 8 studies showed good to excellent reliability, but only one article among them in which accuracy was evaluated had a low risk of bias. The intraclass correlation coefficient (ICC) and Cohen coefficient (κ) varied between 0.44 and 1.00. The accuracy varied between 63.9% and 94.4%. Potential MRI features, such as lamellated hyperintense synovitis, edema, fluid collection, or lymphadenopathy, might be valuable for diagnosing PJIs. Conclusion The quality of the evidence regarding the role of MRI for PJIs diagnosis was low. There is preliminary evidence that MRI has a noteworthy value of distinguishing suspected periprosthetic joint infection in patients with total knee arthroplasty or total hip arthroplasty, but the definition of specific MRI features related to PJIs diagnosis lacks consensus and standardization. Large-scale studies with robust quality were required to help make better clinical decisions in the future.

Incorporating cover crops into cropping systems offers numerous potential benefits, including the reduction of soil erosion, suppression of weeds, decreased nitrogen requirements for subsequent crops, and increased carbon sequestration. The aboveground biomass (AGB) of cover crops strongly influences their performance in delivering these benefits. Despite the significance of AGB, a comprehensive field-based high-throughput phenotyping study to quantify AGB of multiple cover crops in the U.S. Midwest has not been found. This study presents a two-year field experiment carried out in Eastern Nebraska, USA, to estimate AGB of five different cover crop species [canola (Brassica napus L.), rye (Secale cereale L.), triticale (Triticale × Triticosecale L.), vetch (Vicia sativa L.), and wheat (Triticum aestivum L.)] using high-throughput phenotyping and Machine Learning (ML) models. Destructive AGB sampling was performed three times during each spring season in 2022 and 2023. An array of morphological, spectral, thermal, and environmental features from the sensors were utilized as feature inputs of ML models. Moderately strong linear correlations between AGB and the selected features were observed. Four ML models, namely Random Forests Regression (RFR), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), and Artificial Neural Network (ANN), were investigated. Among the four models, PLSR achieved the highest Coefficient of Determination (R2) of 0.84 and the lowest Root Mean Squared Error (RMSE) of 892 kg/ha (Normalized RMSE (NRMSE) = 8.87%), indicating that PLSR could be the most appropriate method for estimating AGB of multiple cover crop species. Feature importance analysis ranked spectral features like Normalized Difference Red Edge (NDRE), Solar-induced Fluorescence (SIF), Spectral Reflectance at 485 nm (R485), and Normalized Difference Vegetation Index (NDVI) as top model features using PLSR. When utilizing fewer feature inputs, ANN exhibited better prediction performance compared to other models. Using morphological and spectral parameters as input features alone led to a R2 of 0.80 and 0.77 for AGB prediction using ANN, respectively. This study demonstrated the feasibility of high-throughput phenotyping and ML techniques for accurately estimating AGB of multiple cover crop species. Further enhancement of model performance could be achieved through additional destructive sampling conducted across multiple locations and years.

In order to improve the ability of micromechanical sensors to update data-matching nodes in real-time and speed up the establishment of a neighbor relationship between nodes, this paper proposes a personalized recommendation algorithm for micromechanical sensors based on the cloud model to improve the stability and real-time performance of micromechanical sensors. The algorithm uses the service attribute values of the cloud computing model and cloud clustering method to set feature term labels and establish a cloud service similarity matrix so as to meet the user-matched manufacturing service requirements. The intra-class clustering technique is applied to measure the clustering effect, and the evaluation function of each clustering number is calculated on the basis of considering the time sequence to determine the best clustering result to complete the personalized recommendation path for micromechanical sensors. To verify the application effect of the personalized recommendation algorithm for micromechanical sensors based on the cloud model, experiments are conducted. The results show that the recommendation algorithm in this paper can always control the node energy consumption below 2.5×103, and the average discovery delay is stable between 41-43 seconds. And the sensor response time is 12.1 seconds, and the average absolute deviation value is 0.23, which is nearly 1.3 times smaller than 0.53 and 0.52 of the collaborative recommendation algorithm and hybrid recommendation algorithm. It can be seen that the recommendation algorithm in this paper solves the problem of excessive neighbor discovery delay in the communication process of micromechanical sensors and effectively improves the personalized recommendation performance of micromechanical sensors.

Advancements in imaging, computer vision, and automation have revolutionized various fields, including field-based high-throughput plant phenotyping (FHTPP). This integration allows for the rapid and accurate measurement of plant traits. Deep Convolutional Neural Networks (DCNNs) have emerged as a powerful tool in FHTPP, particularly in crop segmentation—identifying crops from the background—crucial for trait analysis. However, the effectiveness of DCNNs often hinges on the availability of large, labeled datasets, which poses a challenge due to the high cost of labeling. In this study, a deep learning with bagging approach is introduced to enhance crop segmentation using high-resolution RGB images, tested on the NU-Spidercam dataset from maize plots. The proposed method outperforms traditional machine learning and deep learning models in prediction accuracy and speed. Remarkably, it achieves up to 40% higher Intersection-over-Union (IoU) than the threshold method and 11% over conventional machine learning, with significantly faster prediction times and manageable training duration. Crucially, it demonstrates that even small labeled datasets can yield high accuracy in semantic segmentation. This approach not only proves effective for FHTPP but also suggests potential for broader application in remote sensing, offering a scalable solution to semantic segmentation challenges. This paper is accompanied by publicly available source code.